Validation of a new method for ultrasonic structural health monitoring using advanced signal analysis. This paper presents the results of research on a new method for the monitoring of structural health using ultrasound. Conventional ultrasonic methods use the damping of the first arrival of the echo to determine imperfections, whereas this method uses the total complex echo, which has been subjected to multiple scattering and deflections within the tested material. It is experimentally demonstrated that the method works for health monitoring of a composite flat plate. A reference signal of an undamaged plate was recorded, which was correlated with recorded control signals of a damaged and a doubly damaged plate. To quantify this correlation the parameter fidelity was used. As the control signals are correlated with the reference signal the correlation is supposed to decrease as the plate is damaged and decrease further as the plate is doubly damaged.
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Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
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Work on animals indicates that BOLD is preferentially sensitive to local field potentials, and that it correlates most strongly with gamma band neuronal synchronization. Here we investigate how the BOLD signal in humans performing a cognitive task is related to neuronal synchronization across different frequency bands. We simultaneously recorded EEG and BOLD while subjects engaged in a visual attention task known to induce sustained changes in neuronal synchronization across a wide range of frequencies. Trial-by-trial BOLD fluctuations correlated positively with trial-by-trial fluctuations in high-EEG gamma power (60. -80 Hz) and negatively with alpha and beta power. Gamma power on the one hand, and alpha and beta power on the other hand, independently contributed to explaining BOLD variance. These results indicate that the BOLD-gamma coupling observed in animals can be extrapolated to humans performing a task and that neuronal dynamics underlying high- and low-frequency synchronization contribute independently to the BOLD signal.
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In het project werken onderzoekers van het Lectoraat samen met publieke organisaties toe naar een tool waarmee onderstromen in het publieke debat rondom issues eerder kunnen worden opgemerkt. We exploreren met welk algoritme we patronen in geruchtvorming en mobilisatie kunnen opsporen, en tevens hoe we de interactie tussen newsroom-analisten en de output van een monitoring tool het beste kunnen vormgeven.Doel Het doel van dit project is een brede en structureel toepasbare aanpak van het issuemanagement: Hoe kunnen de communicatieprofessionals van publieke organisaties potentiële issues op sociale media vroegtijdig opmerken? Resultaten We willen dit bereiken door enerzijds kennis en inzicht te vergaren en anderzijds de uitkomsten daarvan voor publieke organisaties te vertalen in praktische handgrepen: tools, handleiding, training. Looptijd 01 oktober 2022 - 30 september 2024 Aanpak Via cases ingebracht door de praktijkpartners en focusgroepen staan we in nauw contact met het consortium. In de eerste werkpakketten onderzoeken we de verschillende cases aan de hand van discoursanalyse. De inzichten die we hierbij opdoen, gebruiken we vervolgens om te bekijken hoe we de interactie tussen mens en machine het beste kunnen vormgeven en wel zo dat er ten behoeve van de communicatie en het management van issues via interactieve visualisaties steeds weer triggers afgegeven worden. Op basis van de opgedane inzichten richten we een interface in. Deze maakt het analisten en communicatieprofessionals mogelijk om vroegtijdig issues te signaleren.
In het project werken onderzoekers van het Lectoraat samen met publieke organisaties toe naar een tool waarmee onderstromen in het publieke debat rondom issues eerder kunnen worden opgemerkt. We exploreren met welk algoritme we patronen in geruchtvorming en mobilisatie kunnen opsporen, en tevens hoe we de interactie tussen newsroom-analisten en de output van een monitoring tool het beste kunnen vormgeven.
Carboxylated cellulose is an important product on the market, and one of the most well-known examples is carboxymethylcellulose (CMC). However, CMC is prepared by modification of cellulose with the extremely hazardous compound monochloracetic acid. In this project, we want to make a carboxylated cellulose that is a functional equivalent for CMC using a greener process with renewable raw materials derived from levulinic acid. Processes to achieve cellulose with a low and a high carboxylation degree will be designed.
Lectoraat, onderdeel van NHL Stenden Hogeschool
